#importing all the required libraries
import pandas as pd
import numpy as np
import os

import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib import style

np.random.seed(42)
from datetime import datetime, timedelta
%matplotlib inline

df  = pd.read_csv('Sample store final.csv', error_bad_lines=False, engine='python')
df

df.columns = df.columns.to_series().apply(lambda x: x.strip())

df = df.copy()

df.head(5)

print(list(df.columns))

['Row_ID', 'Order_ID', 'Order_Date', 'Ship_Date', 'Ship_Mode', 'Customer_ID', 'Customer_Name', 'Segment', 'Country', 'City', 'State', 'Postal_Code', 'Region', 'Product_ID', 'CategoryName', 'Sub-Category', 'Product_Name', 'Sales', 'Quantity', 'Discount', 'Profit', 'Year_of_Order', 'Order_Time', 'Month_of_Order', 'Week_of_Year_for_Order', 'Quarter_of_Year_for_Order', 'Year_of_shipping_date', 'Quarter_of_shipping_date', 'Week_of_year_of_shipment', 'Month_of_shipment', 'Day_of_shipment', 'Day_of_order']

df.dtypes

Row_ID                         int64
Order_ID                      object
Order_Date                    object
Ship_Date                     object
Ship_Mode                     object
Customer_ID                   object
Customer_Name                 object
Segment                       object
Country                       object
City                          object
State                         object
Postal_Code                    int64
Region                        object
Product_ID                    object
CategoryName                  object
Sub-Category                  object
Product_Name                  object
Sales                        float64
Quantity                       int64
Discount                     float64
Profit                       float64
Year_of_Order                  int64
Order_Time                    object
Month_of_Order                object
Week_of_Year_for_Order         int64
Quarter_of_Year_for_Order      int64
Year_of_shipping_date          int64
Quarter_of_shipping_date       int64
Week_of_year_of_shipment       int64
Month_of_shipment             object
Day_of_shipment               object
Day_of_order                  object
dtype: object

sales_per_purchase_month = df.groupby(['Month_of_Order', 'Day_of_order'], as_index=False).Sales.sum()
sales_per_purchase_month = sales_per_purchase_month.sort_values(by=['Month_of_Order'], ascending=True)

Let's Take a Peep at Our Weekly and Monthly Sales Trend Over All The Four Consecutive Years In Our Historical Database

import plotly.express as px

salesdata = sales_per_purchase_month
fig = px.line(df, x="Month_of_Order", y="Sales", color='Day_of_order', title='Sales Each Month & Each DayofWeek')

fig.update_layout(
    title="Sales Each Month & Each DayofWeek",
    xaxis_title="Months",
    yaxis_title="Sales(in $$)",
    font=dict(
        family="Courier New, monospace",
        size=15,
        color="#7f7f7f"
    )
)
fig.show()

# creating an aggregation
avg_sales_per_category = df.groupby('CategoryName', as_index=False).agg({'Sales': ['count', 'mean']})
avg_sales_per_category.columns = ['Product Category', 'Total Sales', 'Average Sales']


avg_sales_per_category = avg_sales_per_category[avg_sales_per_category['Total Sales'] > 100]
avg_sales_per_category = avg_sales_per_category.sort_values(by='Total Sales', ascending=False)
avg_sales_per_category

Let's View Our Top Categories by Average Sales Made Over a Span of 4 Consecutive Years

import plotly.express as px
avg_sales = avg_sales_per_category[:20]
fig = px.bar(avg_sales, x='Product Category', y='Total Sales',
             hover_data=['Average Sales'], color='Average Sales',
             height=500)
fig.show()

# creating an aggregation

avg_profit_per_category = df.groupby('CategoryName', as_index=False).agg({'Profit': ['count', 'mean']})
avg_profit_per_category.columns = ['Product Category', 'Total Profit', 'Average Profit']


avg_profit_per_category = avg_profit_per_category[avg_profit_per_category['Total Profit'] > 100]
avg_profit_per_category = avg_profit_per_category.sort_values(by='Total Profit', ascending=False)
avg_profit_per_category

Top Categories by Average Profits Generated

import plotly.express as px
avg_profit = avg_profit_per_category[:20]
fig = px.bar(avg_profit, x='Product Category', y='Average Profit',
             hover_data=['Total Profit'], color='Total Profit',
             height=500)
fig.show()

# Keeping track of profits made using our knowledge of time
purchaseday = df.copy()
# creating an aggregation 
profit_per_purchase_month = df.groupby(['Month_of_Order', 'Quarter_of_Year_for_Order', 'Day_of_order'], as_index=False).Profit.sum()
profit_per_purchase_month = profit_per_purchase_month.sort_values(by=['Month_of_Order'], ascending=True)
profit_per_purchase_month

# creating a purchase day feature alongside aggregration using our knowledge of time
sales_per_month = df.groupby(['Month_of_Order', 'Day_of_order','Quarter_of_Year_for_Order'], as_index=False).Sales.sum()
sales_per__month = sales_per_purchase_month.sort_values(by=['Month_of_Order'], ascending=True)
sales_per_month

# creating a purchase day feature
df = df.copy()
# creating an aggregation


sales_per_month = df.groupby(['Month_of_Order', 'Day_of_order'], as_index=False).Sales.mean()

sales_per_month

sales_per_month = sales_per_month.sort_values(by=['Day_of_order'], ascending=True)
sales_per_month

sales_per_category = df.groupby(['Month_of_Order', 'Product_Name'], as_index=False).Sales.sum()
sales_per_category = sales_per_category.sort_values(by=['Sales'], ascending=False)
sales_per_category.columns = ['Purchase Month','Product Category', 'Sales Revenue']
sales_per_category

import plotly.express as px

sales_per_category = df.groupby(['Month_of_Order', 'CategoryName'], as_index=False).Sales.sum()
sales_per_category = sales_per_category.sort_values(by=['Sales'], ascending=False)
sales_per_category.columns = ['Purchase_Month','CategoryName', 'Sales_Revenue']
sales_per_category

An Interactive and Insightful View of Our Top Categories by Sales Revenue

import plotly.express as px

df = sales_per_category
fig = px.bar(df, y='Sales_Revenue', x='CategoryName', text='Sales_Revenue', hover_data=['Purchase_Month'])
fig.update_traces(texttemplate='%{text:.2s}', textposition='outside')
fig.update_layout(barmode='stack',uniformtext_minsize=8, uniformtext_mode='hide')
fig.show()

df  = pd.read_csv('Sample store final.csv', error_bad_lines=False, engine='python')
df

sales_per_category = df.groupby(['Month_of_Order', 'CategoryName'], as_index=False).Sales.sum()
sales_per_category = sales_per_category.sort_values(by=['Sales'], ascending=False)
sales_per_category.columns = ['Purchase_Month','CategoryName', 'Sales_Revenue']
sales_per_category

sales_per_category = df.groupby(['Month_of_Order', 'Product_Name'], as_index=False).Sales.sum()
sales_per_category = sales_per_category.sort_values(by=['Sales'], ascending=False)
sales_per_category.columns = ['Purchase_Month','Product_Name', 'Sales_Revenue']
sales_per_category

Metrics For Digital Marketing Dashboard

Brand/Category Performance Over Years

Horizontal Metrics For Dashboard (YTD FOR 2014 To 2017)

total_rev_year = df.groupby(['Year_of_Order'], as_index=False).Sales.sum()
#total_rev_year = total_rev_year[total_rev_year['Year_of_Order']== 2018]
total_rev_year

total_orders = df.groupby(['Year_of_Order'], as_index=False).Order_ID.nunique()
total_orders

0     969
1    1038
2    1315
3    1687
Name: Order_ID, dtype: int64

total_category = df.Product_Name.nunique()
total_category

1850

total_customers = df.Customer_ID.nunique()
total_customers

793

df['Year_of_Order'].value_counts()

2017    3312
2016    2587
2015    2102
2014    1993
Name: Year_of_Order, dtype: int64

Vertical Metrics For Dashboard

import plotly.graph_objects as go

df = df[df.Year_of_Order == 2014]
sales_per_category = df.groupby(['CategoryName'], as_index=False).Sales.sum()
sales_per_category = sales_per_category.sort_values(by=['Sales'], ascending=False)
sales_per_category.columns = ['Product Category', 'Sales_Revenue']

sales_per_category = sales_per_category[:20]
labels = sales_per_category['Product Category']
values = sales_per_category['Sales_Revenue']

# Use `hole` to create a donut-like pie chart
fig = go.Figure(data=[go.Pie(labels=labels, values=values, hole=.3)])
fig.show()

df  = pd.read_csv('Sample store final.csv', error_bad_lines=False, engine='python')
df

Total Sales By Category For Individual Year

import plotly.graph_objects as go

df = df[df.Year_of_Order == 2015]
sales_per_category = df.groupby(['CategoryName'], as_index=False).Sales.sum()
sales_per_category = sales_per_category.sort_values(by=['Sales'], ascending=False)
sales_per_category.columns = ['Product Category', 'Sales_Revenue']

sales_per_category = sales_per_category[:20]
labels = sales_per_category['Product Category']
values = sales_per_category['Sales_Revenue']

# Use `hole` to create a donut-like pie chart
fig = go.Figure(data=[go.Pie(labels=labels, values=values, hole=.3)])
fig.show()

import plotly.graph_objects as go

df = df[df.Year_of_Order == 2017]
sales_per_category = df.groupby(['CategoryName'], as_index=False).Sales.sum()
sales_per_category = sales_per_category.sort_values(by=['Sales'], ascending=False)
sales_per_category.columns = ['Product Category', 'Sales_Revenue']

sales_per_category = sales_per_category[:20]
labels = sales_per_category['Product Category']
values = sales_per_category['Sales_Revenue']

# Use `hole` to create a donut-like pie chart
fig = go.Figure(data=[go.Pie(labels=labels, values=values, hole=.3)])
fig.show()

import plotly.graph_objects as go

df = df[df.Year_of_Order == 2016]
sales_per_category = df.groupby(['CategoryName'], as_index=False).Sales.sum()
sales_per_category = sales_per_category.sort_values(by=['Sales'], ascending=False)
sales_per_category.columns = ['Product Category', 'Sales Revenue']

sales_per_category = sales_per_category[:20]
labels = sales_per_category['Product Category']
values = sales_per_category['Sales Revenue']

# Use `hole` to create a donut-like pie chart
fig = go.Figure(data=[go.Pie(labels=labels, values=values, hole=.3)])
fig.show()

df  = pd.read_csv('Sample store final.csv', error_bad_lines=False, engine='python')
df

total_rev_month = df.groupby(['Year_of_Order', 'Month_of_Order', 'CategoryName'], as_index=False).Sales.sum()
total_rev_month.columns = ['Sales Year','Sales Month','Product Category' , 'Sales Revenue']
total_rev_month

Category Sales Across Years

import plotly.express as px
import numpy as np
df = total_rev_month
fig = px.sunburst(df, path=['Sales Year', 'Product Category'], values='Sales Revenue',
                  color='Sales Revenue', hover_data=['Sales Revenue'],
                  color_continuous_scale='RdBu',
                  color_continuous_midpoint=np.average(df['Sales Revenue'], weights=df['Sales Revenue']))
fig.show()

total_profit_month = df.groupby(['Year_of_Order', 'Month_of_Order', 'CategoryName'], as_index=False).Profit.sum()
#total_rev_month = total_rev_month.sort_values(by=['order_purchase_year'], ascending=True)
total_profit_month.columns = ['Profit for Year','Sales Month','Product Category' , 'Profit']
total_profit_month

import plotly.express as px
import numpy as np
df = total_profit_month
fig = px.sunburst(df, path=['Profit for Year', 'Product Category'], values='Profit',
                  color='Profit', hover_data=['Profit'],
                  color_continuous_scale='RdBu',
                  color_continuous_midpoint=np.average(df['Profit'], weights=df['Profit']))
fig.show()

Let's Draw Insights From Our Weekday Sales For Each Individual Year

total_weekday_rev = df[df['Year_of_Order'] == 2014].groupby(['Day_of_order', 'CategoryName'], as_index=False).Sales.sum()
#total_rev_month = total_rev_month.sort_values(by=['order_purchase_year'], ascending=True)
total_weekday_rev.columns = ['Day_of_order','Product Category' , 'Sales Revenue']
total_weekday_rev

#Year 2014
labels = total_weekday_rev['Day_of_order']
values = total_weekday_rev['Sales Revenue']

# Use `hole` to create a donut-like pie chart
fig = go.Figure(data=[go.Pie(labels=labels, values=values, hole=.3)])
fig.show()

df = total_weekday_rev
fig = px.sunburst(df, path=['Day_of_order', 'Product Category'], values='Sales Revenue',
                  color='Sales Revenue', hover_data=['Product Category'],
                  color_continuous_scale='RdBu',
                  color_continuous_midpoint=np.average(df['Sales Revenue'], weights=df['Sales Revenue']))
fig.show()

total_weekday_rev = df[df['Year_of_Order'] == 2015].groupby(['Day_of_order', 'CategoryName'], as_index=False).Sales.sum()
#total_rev_month = total_rev_month.sort_values(by=['order_purchase_year'], ascending=True)
total_weekday_rev.columns = ['Day of Order','Product Category' , 'Sales Revenue']
total_weekday_rev

#Year 2015
labels = total_weekday_rev['Day of Order']
values = total_weekday_rev['Sales Revenue']

# Use `hole` to create a donut-like pie chart
fig = go.Figure(data=[go.Pie(labels=labels, values=values, hole=.3)])
fig.show()

df = total_weekday_rev
fig = px.sunburst(df, path=['Day of Order', 'Product Category'], values='Sales Revenue',
                  color='Sales Revenue', hover_data=['Product Category'],
                  color_continuous_scale='RdBu',
                  color_continuous_midpoint=np.average(df['Sales Revenue'], weights=df['Sales Revenue']))
fig.show()

total_weekday_rev = df[df['Year_of_Order'] == 2016].groupby(['Day_of_order', 'CategoryName'], as_index=False).Sales.sum()
#total_rev_month = total_rev_month.sort_values(by=['order_purchase_year'], ascending=True)
total_weekday_rev.columns = ['Day of Order','Product Category' , 'Sales Revenue']
total_weekday_rev

df = total_weekday_rev
fig = px.sunburst(df, path=['Day of Order', 'Product Category'], values='Sales Revenue',
                  color='Sales Revenue', hover_data=['Product Category'],
                  color_continuous_scale='RdBu',
                  color_continuous_midpoint=np.average(df['Sales Revenue'], weights=df['Sales Revenue']))
fig.show()

total_weekday_rev = df[df['Year_of_Order'] == 2017].groupby(['Day_of_order', 'CategoryName'], as_index=False).Sales.sum()
#total_rev_month = total_rev_month.sort_values(by=['order_purchase_year'], ascending=True)
total_weekday_rev.columns = ['Day of Order','Product Category' , 'Sales Revenue']
total_weekday_rev

#Year 2017
labels = total_weekday_rev['Day of Order']
values = total_weekday_rev['Sales Revenue']

# Use `hole` to create a donut-like pie chart
fig = go.Figure(data=[go.Pie(labels=labels, values=values, hole=.3)])
fig.show()

df = total_weekday_rev
fig = px.sunburst(df, path=['Day of Order', 'Product Category'], values='Sales Revenue',
                  color='Sales Revenue', hover_data=['Product Category'],
                  color_continuous_scale='RdBu',
                  color_continuous_midpoint=np.average(df['Sales Revenue'], weights=df['Sales Revenue']))
fig.show()

Category Activity Alongside Total Purchases

cat_activity = df[df['Year_of_Order'] == 2015].groupby(['CategoryName']).Order_ID.nunique()
#cat_activity = cat_activity.sort_values(['Activity'], ascending=True)
cat_activity = pd.DataFrame(cat_activity)
cat_activity.columns = [ 'Activity']
cat_activity = cat_activity.sort_values(by=['Activity'], ascending=False)
cat_activity

#Year 2015
fig = px.funnel_area(names=cat_activity.index,
                    values=cat_activity.Activity)
fig.show()

cat_activity = df[df['Year_of_Order'] == 2016].groupby(['CategoryName']).Order_ID.nunique()
#cat_activity = cat_activity.sort_values(['Activity'], ascending=True)
cat_activity = pd.DataFrame(cat_activity)
cat_activity.columns = [ 'Activity']
cat_activity = cat_activity.sort_values(by=['Activity'], ascending=False)
cat_activity

#Year 2016
fig = px.funnel_area(names=cat_activity.index,
                    values=cat_activity.Activity)
fig.show()

cat_activity = df[df['Year_of_Order'] == 2014].groupby(['CategoryName']).Order_ID.nunique()
#cat_activity = cat_activity.sort_values(['Activity'], ascending=True)
cat_activity = pd.DataFrame(cat_activity)
cat_activity.columns = [ 'Activity']
cat_activity = cat_activity.sort_values(by=['Activity'], ascending=False)
cat_activity

#Year 2014
fig = px.funnel_area(names=cat_activity.index,
                    values=cat_activity.Activity)
fig.show()

cat_activity = df[df['Year_of_Order'] == 2017].groupby(['CategoryName']).Order_ID.nunique()
#cat_activity = cat_activity.sort_values(['Activity'], ascending=True)
cat_activity = pd.DataFrame(cat_activity)
cat_activity.columns = [ 'Activity']
cat_activity = cat_activity.sort_values(by=['Activity'], ascending=False)
cat_activity

#Year 2014
fig = px.funnel_area(names=cat_activity.index,
                    values=cat_activity.Activity)
fig.show()

cat_activity = df[df['Year_of_Order'] == 2014].groupby(['Region']).Sales.nunique()
#cat_activity = cat_activity.sort_values(['Activity'], ascending=True)
cat_activity = pd.DataFrame(cat_activity)
cat_activity.columns = [ 'Activity']
cat_activity = cat_activity.sort_values(by=['Activity'], ascending=False)
cat_activity

cat_activity = df[df['Year_of_Order'] == 2015].groupby(['Region']).Sales.nunique()
#cat_activity = cat_activity.sort_values(['Activity'], ascending=True)
cat_activity = pd.DataFrame(cat_activity)
cat_activity.columns = [ 'Activity']
cat_activity = cat_activity.sort_values(by=['Activity'], ascending=False)
cat_activity

#Year 2015
fig = px.funnel_area(names=cat_activity.index,
                    values=cat_activity.Activity)
fig.show()

cat_activity = df[df['Year_of_Order'] == 2016].groupby(['Region']).Sales.nunique()
#cat_activity = cat_activity.sort_values(['Activity'], ascending=True)
cat_activity = pd.DataFrame(cat_activity)
cat_activity.columns = [ 'Activity']
cat_activity = cat_activity.sort_values(by=['Activity'], ascending=False)
cat_activity

#Year 2016
fig = px.funnel_area(names=cat_activity.index,
                    values=cat_activity.Activity)
fig.show()

cat_activity = df[df['Year_of_Order'] == 2017].groupby(['Region']).Sales.nunique()
#cat_activity = cat_activity.sort_values(['Activity'], ascending=True)
cat_activity = pd.DataFrame(cat_activity)
cat_activity.columns = [ 'Activity']
cat_activity = cat_activity.sort_values(by=['Activity'], ascending=False)
cat_activity

#Year 2017
fig = px.funnel_area(names=cat_activity.index,
                    values=cat_activity.Activity)
fig.show()

cat_activity = df[df['Year_of_Order'] == 2014].groupby(['State']).Order_ID.nunique()
#cat_activity = cat_activity.sort_values(['Activity'], ascending=True)
cat_activity = pd.DataFrame(cat_activity)
cat_activity.columns = [ 'Activity']
cat_activity = cat_activity.sort_values(by=['Activity'], ascending=False)
cat_activity

 #Year 2014
fig = px.funnel_area(names=cat_activity.index,
                    values=cat_activity.Activity)
fig.show()

cat_activity = df[df['Year_of_Order'] == 2015].groupby(['State']).Order_ID.nunique()
#cat_activity = cat_activity.sort_values(['Activity'], ascending=True)
cat_activity = pd.DataFrame(cat_activity)
cat_activity.columns = [ 'Activity']
cat_activity = cat_activity.sort_values(by=['Activity'], ascending=False)
cat_activity

 #Year 2015
fig = px.funnel_area(names=cat_activity.index,
                    values=cat_activity.Activity)
fig.show()

cat_activity = df[df['Year_of_Order'] == 2016].groupby(['State']).Order_ID.nunique()
#cat_activity = cat_activity.sort_values(['Activity'], ascending=True)
cat_activity = pd.DataFrame(cat_activity)
cat_activity.columns = [ 'Activity']
cat_activity = cat_activity.sort_values(by=['Activity'], ascending=False)
cat_activity

 #Year 2016
fig = px.funnel_area(names=cat_activity.index,
                    values=cat_activity.Activity)
fig.show()

cat_activity = df[df['Year_of_Order'] == 2017].groupby(['State']).Order_ID.nunique()
#cat_activity = cat_activity.sort_values(['Activity'], ascending=True)
cat_activity = pd.DataFrame(cat_activity)
cat_activity.columns = [ 'Activity']
cat_activity = cat_activity.sort_values(by=['Activity'], ascending=False)
cat_activity

 #Year 2016
fig = px.funnel_area(names=cat_activity.index,
                    values=cat_activity.Activity)
fig.show()

cat_activity = df[df['Year_of_Order'] == 2014].groupby(['City']).Order_ID.nunique()
#cat_activity = cat_activity.sort_values(['Activity'], ascending=True)
cat_activity = pd.DataFrame(cat_activity)
cat_activity.columns = [ 'Activity']
cat_activity = cat_activity.sort_values(by=['Activity'], ascending=False)
cat_activity

total_year_rev = df[df['Year_of_Order'] == 2014].groupby(['Month_of_Order', 'CategoryName'], as_index=False).Sales.sum()
#total_rev_month = total_rev_month.sort_values(by=['order_purchase_year'], ascending=True)
total_year_rev.columns = ['Month_of_order','CategoryName' , 'Sales Revenue']
total_year_rev

#Year 2014
df = total_year_rev
fig = px.sunburst(df, path=['Sales Revenue',  'CategoryName'], values='Sales Revenue',
                  color='Sales Revenue', hover_data=['CategoryName'],
                  color_continuous_scale='RdBu',
                  color_continuous_midpoint=np.average(df['Sales Revenue'], weights=df['Sales Revenue']))
fig.show()

total_year_rev = df[df['Year_of_Order'] == 2015].groupby(['Month_of_Order', 'CategoryName'], as_index=False).Sales.sum()
#total_rev_month = total_rev_month.sort_values(by=['order_purchase_year'], ascending=True)
total_year_rev.columns = ['Month_of_order','CategoryName' , 'Sales Revenue']
total_year_rev

Let's Take a Glimpse at Our Sales Revenue For All Product Category For each Individual Year

#Year 2015
df = total_year_rev
fig = px.sunburst(df, path=['Sales Revenue',  'CategoryName'], values='Sales Revenue',
                  color='Sales Revenue', hover_data=['CategoryName'],
                  color_continuous_scale='RdBu',
                  color_continuous_midpoint=np.average(df['Sales Revenue'], weights=df['Sales Revenue']))
fig.show()

total_year_rev = df[df['Year_of_Order'] == 2016].groupby(['Month_of_Order', 'CategoryName'], as_index=False).Sales.sum()
#total_rev_month = total_rev_month.sort_values(by=['order_purchase_year'], ascending=True)
total_year_rev.columns = ['Month_of_order','CategoryName' , 'Sales Revenue']
total_year_rev

#Year 2016
df = total_year_rev
fig = px.sunburst(df, path=['Sales Revenue',  'CategoryName'], values='Sales Revenue',
                  color='Sales Revenue', hover_data=['CategoryName'],
                  color_continuous_scale='RdBu',
                  color_continuous_midpoint=np.average(df['Sales Revenue'], weights=df['Sales Revenue']))
fig.show()

total_year_rev = df[df['Year_of_Order'] == 2017].groupby(['Month_of_Order', 'CategoryName'], as_index=False).Sales.sum()
#total_rev_month = total_rev_month.sort_values(by=['order_purchase_year'], ascending=True)
total_year_rev.columns = ['Month_of_order','CategoryName' , 'Sales Revenue']
total_year_rev

#Year 2017
df = total_rev_hour
fig = px.sunburst(df, path=['Sales Revenue',  'CategoryName'], values='Sales Revenue',
                  color='Sales Revenue', hover_data=['CategoryName'],
                  color_continuous_scale='RdBu',
                  color_continuous_midpoint=np.average(df['Sales Revenue'], weights=df['Sales Revenue']))
fig.show()

Take a Peep at Profits Made For Each Individual Year

total_year_profit = df[df['Year_of_Order'] == 2014].groupby(['Month_of_Order', 'CategoryName'], as_index=False).Profit.sum()
total_year_profit.columns = ['Month_of_order','CategoryName' , 'Profit']
total_year_profit

total_year_profit = df[df['Year_of_Order'] == 2014].groupby(['Month_of_Order', 'CategoryName'], as_index=False).Profit.sum()
total_year_profit.columns = ['Month_of_order','CategoryName' , 'Profit']
total_year_profit

#Year 2014
df = total_year_profit
fig = px.sunburst(df, path=['Profit',  'CategoryName'], values='Profit',
                  color='Profit', hover_data=['CategoryName'],
                  color_continuous_scale='RdBu',
                  color_continuous_midpoint=np.average(df['Profit'], weights=df['Profit']))
fig.show()

total_year_profit = df[df['Year_of_Order'] == 2015].groupby(['Month_of_Order', 'CategoryName'], as_index=False).Profit.sum()
total_year_profit.columns = ['Month_of_order','CategoryName' , 'Profit']
total_year_profit

#Year 2015
df = total_year_profit
fig = px.sunburst(df, path=['Profit',  'CategoryName'], values='Profit',
                  color='Profit', hover_data=['CategoryName'],
                  color_continuous_scale='RdBu',
                  color_continuous_midpoint=np.average(df['Profit'], weights=df['Profit']))
fig.show()

total_year_profit = df[df['Year_of_Order'] == 2015].groupby(['Month_of_Order', 'CategoryName'], as_index=False).Profit.sum()
total_year_profit.columns = ['Month_of_order','CategoryName' , 'Profit']
total_year_profit

#Year 2015
df = total_year_profit
fig = px.sunburst(df, path=['Profit',  'CategoryName'], values='Profit',
                  color='Profit', hover_data=['CategoryName'],
                  color_continuous_scale='RdBu',
                  color_continuous_midpoint=np.average(df['Profit'], weights=df['Profit']))
fig.show()

total_year_profit = df[df['Year_of_Order'] == 2016].groupby(['Month_of_Order', 'CategoryName'], as_index=False).Profit.sum()
total_year_profit.columns = ['Month_of_order','CategoryName' , 'Profit']
total_year_profit

#Year 2016
df = total_year_profit
fig = px.sunburst(df, path=['Profit',  'CategoryName'], values='Profit',
                  color='Profit', hover_data=['CategoryName'],
                  color_continuous_scale='RdBu',
                  color_continuous_midpoint=np.average(df['Profit'], weights=df['Profit']))
fig.show()

total_year_profit = df[df['Year_of_Order'] == 2017].groupby(['Month_of_Order', 'CategoryName'], as_index=False).Profit.sum()
total_year_profit.columns = ['Month_of_order','CategoryName' , 'Profit']
total_year_profit

#Year 2017
df = total_year_profit
fig = px.sunburst(df, path=['Profit',  'CategoryName'], values='Profit',
                  color='Profit', hover_data=['CategoryName'],
                  color_continuous_scale='RdBu',
                  color_continuous_midpoint=np.average(df['Profit'], weights=df['Profit']))
fig.show()

	Row_ID	Order_ID	Order_Date	Ship_Date	Ship_Mode	Customer_ID	Customer_Name	Segment	Country	City	...	Order_Time	Month_of_Order	Week_of_Year_for_Order	Quarter_of_Year_for_Order	Year_of_shipping_date	Quarter_of_shipping_date	Week_of_year_of_shipment	Month_of_shipment	Day_of_shipment	Day_of_order
0	7981	CA-2014-103800	1/3/2014 0:00	1/7/2014 0:00	Standard Class	DP-13000	Darren Powers	Consumer	United States	Houston	...	12:00:00 AM	January	1	1	2014	1	2	January	Tuesday	Friday
1	740	CA-2014-112326	1/4/2014 0:00	1/8/2014 0:00	Standard Class	PO-19195	Phillina Ober	Home Office	United States	Naperville	...	12:00:00 AM	January	1	1	2014	1	2	January	Wednesday	Saturday
2	741	CA-2014-112326	1/4/2014 0:00	1/8/2014 0:00	Standard Class	PO-19195	Phillina Ober	Home Office	United States	Naperville	...	12:00:00 AM	January	1	1	2014	1	2	January	Wednesday	Saturday
3	742	CA-2014-112326	1/4/2014 0:00	1/8/2014 0:00	Standard Class	PO-19195	Phillina Ober	Home Office	United States	Naperville	...	12:00:00 AM	January	1	1	2014	1	2	January	Wednesday	Saturday
4	1760	CA-2014-141817	1/5/2014 0:00	1/12/2014 0:00	Standard Class	MB-18085	Mick Brown	Consumer	United States	Philadelphia	...	12:00:00 AM	January	2	1	2014	1	3	January	Sunday	Sunday
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
9989	908	CA-2017-143259	12/30/2017 0:00	1/3/2018 0:00	Standard Class	PO-18865	Patrick O'Donnell	Consumer	United States	New York City	...	12:00:00 AM	December	52	4	2018	1	1	January	Wednesday	Saturday
9990	909	CA-2017-143259	12/30/2017 0:00	1/3/2018 0:00	Standard Class	PO-18865	Patrick O'Donnell	Consumer	United States	New York City	...	12:00:00 AM	December	52	4	2018	1	1	January	Wednesday	Saturday
9991	1297	CA-2017-115427	12/30/2017 0:00	1/3/2018 0:00	Standard Class	EB-13975	Erica Bern	Corporate	United States	Fairfield	...	12:00:00 AM	December	52	4	2018	1	1	January	Wednesday	Saturday
9992	1298	CA-2017-115427	12/30/2017 0:00	1/3/2018 0:00	Standard Class	EB-13975	Erica Bern	Corporate	United States	Fairfield	...	12:00:00 AM	December	52	4	2018	1	1	January	Wednesday	Saturday
9993	5092	CA-2017-156720	12/30/2017 0:00	1/3/2018 0:00	Standard Class	JM-15580	Jill Matthias	Consumer	United States	Loveland	...	12:00:00 AM	December	52	4	2018	1	1	January	Wednesday	Saturday

	Row_ID	Order_ID	Order_Date	Ship_Date	Ship_Mode	Customer_ID	Customer_Name	Segment	Country	City	...	Order_Time	Month_of_Order	Week_of_Year_for_Order	Quarter_of_Year_for_Order	Year_of_shipping_date	Quarter_of_shipping_date	Week_of_year_of_shipment	Month_of_shipment	Day_of_shipment	Day_of_order
0	7981	CA-2014-103800	1/3/2014 0:00	1/7/2014 0:00	Standard Class	DP-13000	Darren Powers	Consumer	United States	Houston	...	12:00:00 AM	January	1	1	2014	1	2	January	Tuesday	Friday
1	740	CA-2014-112326	1/4/2014 0:00	1/8/2014 0:00	Standard Class	PO-19195	Phillina Ober	Home Office	United States	Naperville	...	12:00:00 AM	January	1	1	2014	1	2	January	Wednesday	Saturday
2	741	CA-2014-112326	1/4/2014 0:00	1/8/2014 0:00	Standard Class	PO-19195	Phillina Ober	Home Office	United States	Naperville	...	12:00:00 AM	January	1	1	2014	1	2	January	Wednesday	Saturday
3	742	CA-2014-112326	1/4/2014 0:00	1/8/2014 0:00	Standard Class	PO-19195	Phillina Ober	Home Office	United States	Naperville	...	12:00:00 AM	January	1	1	2014	1	2	January	Wednesday	Saturday
4	1760	CA-2014-141817	1/5/2014 0:00	1/12/2014 0:00	Standard Class	MB-18085	Mick Brown	Consumer	United States	Philadelphia	...	12:00:00 AM	January	2	1	2014	1	3	January	Sunday	Sunday

	Product Category	Total Sales	Average Sales
1	Office Supplies	6026	119.324101
0	Furniture	2121	349.834887
2	Technology	1847	452.709276

	Product Category	Total Profit	Average Profit
1	Office Supplies	6026	20.327050
0	Furniture	2121	8.699327
2	Technology	1847	78.752002

	Month_of_Order	Quarter_of_Year_for_Order	Day_of_order	Profit
0	April	2	Friday	-273.1101
1	April	2	Monday	-1027.7799
2	April	2	Saturday	7860.9157
3	April	2	Sunday	-9.0884
4	April	2	Thursday	1795.4848
...	...	...	...	...
78	September	3	Monday	5294.0876
79	September	3	Saturday	3897.2571
80	September	3	Sunday	7321.1271
81	September	3	Thursday	4670.9605
83	September	3	Wednesday	2513.7373

	Month_of_Order	Day_of_order	Quarter_of_Year_for_Order	Sales
0	April	Friday	2	22441.5335
1	April	Monday	2	20433.2300
2	April	Saturday	2	40912.0760
3	April	Sunday	2	18378.0135
4	April	Thursday	2	15289.8226
...	...	...	...	...
79	September	Saturday	3	39635.9630
80	September	Sunday	3	55147.9550
81	September	Thursday	3	42917.0974
82	September	Tuesday	3	36624.3308
83	September	Wednesday	3	11553.2230

	Month_of_Order	Day_of_order	Sales
0	April	Friday	182.451492
1	April	Monday	206.396263
2	April	Saturday	338.116331
3	April	Sunday	141.369335
4	April	Thursday	186.461251
...	...	...	...
79	September	Saturday	167.240350
80	September	Sunday	222.370786
81	September	Thursday	212.460878
82	September	Tuesday	256.114201
83	September	Wednesday	275.076738

	Purchase Month	Product Category	Sales Revenue
6178	October	Canon imageCLASS 2200 Advanced Copier	28699.918
4019	March	Cisco TelePresence System EX90 Videoconferenci...	22638.480
4004	March	Canon imageCLASS 2200 Advanced Copier	13999.960
1471	December	GBC Ibimaster 500 Manual ProClick Binding System	11490.798
7186	September	Lexmark MX611dhe Monochrome Laser Printer	11219.934
...	...	...	...
6408	October	Maxell 4.7GB DVD-R 5/Pack	0.990
3356	June	Avery Triangle Shaped Sheet Lifters, Black, 2/...	0.984
6711	September	Acco 3-Hole Punch	0.876
3343	June	Avery Durable Slant Ring Binders With Label Ho...	0.836
3565	June	Hoover Replacement Belt for Commercial Guardsm...	0.444

	Purchase_Month	CategoryName	Sales_Revenue
29	November	Technology	131134.8410
6	December	Furniture	121817.9675
27	November	Furniture	121286.0490
33	September	Furniture	106380.5907
7	December	Office Supplies	102642.6440
34	September	Office Supplies	101888.6460
8	December	Technology	100832.8920
28	November	Office Supplies	100040.1810
35	September	Technology	99380.7090
23	March	Technology	97851.5700
32	October	Technology	87031.9450
26	May	Technology	63642.1550
4	August	Office Supplies	62134.1620
30	October	Furniture	58087.5157
22	March	Office Supplies	56385.6430
31	October	Office Supplies	55203.5240
17	July	Technology	54854.0260
18	June	Furniture	52999.4633
5	August	Technology	52025.8560
19	June	Office Supplies	51414.6330
21	March	Furniture	50768.2758
1	April	Office Supplies	49433.2700
15	July	Furniture	49377.0120
24	May	Furniture	48365.1307
20	June	Technology	48304.5830
2	April	Technology	47630.2750
3	August	Furniture	44884.0450
25	May	Office Supplies	43021.5260
16	July	Office Supplies	43007.0590
0	April	Furniture	40698.5836
13	January	Office Supplies	33233.8310
12	January	Furniture	31569.2416
14	January	Technology	30121.7630
11	February	Technology	23343.4180
10	February	Office Supplies	20641.9130
9	February	Furniture	15765.9204

	Year_of_Order	Sales
0	2014	484247.4981
1	2015	470532.5090
2	2016	609205.5980
3	2017	733215.2552

	Day_of_order	Product Category	Sales Revenue
0	Friday	Furniture	29898.5326
1	Friday	Office Supplies	24843.0840
2	Friday	Technology	29865.7490
3	Monday	Furniture	43010.1400
4	Monday	Office Supplies	32885.2530
5	Monday	Technology	26629.2940
6	Saturday	Furniture	22099.8520
7	Saturday	Office Supplies	25744.4840
8	Saturday	Technology	22875.1970
9	Sunday	Furniture	18006.1435
10	Sunday	Office Supplies	19008.7760
11	Sunday	Technology	23533.2740
12	Thursday	Furniture	775.8068
13	Thursday	Office Supplies	877.3960
14	Thursday	Technology	4443.2780
15	Tuesday	Furniture	28805.9280
16	Tuesday	Office Supplies	36555.3770
17	Tuesday	Technology	47387.5460
18	Wednesday	Furniture	14596.4502
19	Wednesday	Office Supplies	11862.0420
20	Wednesday	Technology	20543.8950

	Day of Order	Product Category	Sales Revenue
0	Friday	Furniture	23028.0777
1	Friday	Office Supplies	22366.9780
2	Friday	Technology	39778.1480
3	Monday	Furniture	31682.2280
4	Monday	Office Supplies	30179.4910
5	Monday	Technology	29149.6380
6	Saturday	Furniture	17831.9494
7	Saturday	Office Supplies	22790.9930
8	Saturday	Technology	21769.4900
9	Sunday	Furniture	36397.5516
10	Sunday	Office Supplies	18607.2400
11	Sunday	Technology	29257.6110
12	Thursday	Furniture	37144.8033
13	Thursday	Office Supplies	22396.2990
14	Thursday	Technology	24008.7640
15	Tuesday	Furniture	19865.3330
16	Tuesday	Office Supplies	19340.8280
17	Tuesday	Technology	15669.5580
18	Wednesday	Furniture	4568.2940
19	Wednesday	Office Supplies	1551.6340
20	Wednesday	Technology	3147.6000

	Day of Order	Product Category	Sales Revenue
0	Friday	Furniture	39789.9775
1	Friday	Office Supplies	35635.8630
2	Friday	Technology	39641.2550
3	Monday	Furniture	36911.3115
4	Monday	Office Supplies	27403.0910
5	Monday	Technology	36573.1830
6	Saturday	Furniture	30976.9518
7	Saturday	Office Supplies	33047.6930
8	Saturday	Technology	33896.4130
9	Sunday	Furniture	31654.6168
10	Sunday	Office Supplies	35099.8730
11	Sunday	Technology	51073.6840
12	Thursday	Furniture	35316.4426
13	Thursday	Office Supplies	33696.6210
14	Thursday	Technology	30071.8340
15	Tuesday	Furniture	21919.3658
16	Tuesday	Office Supplies	15444.5790
17	Tuesday	Technology	32402.0690
18	Wednesday	Furniture	2332.7700
19	Wednesday	Office Supplies	3612.2620
20	Wednesday	Technology	2705.7420

	Day of Order	Product Category	Sales Revenue
0	Friday	Furniture	42940.6595
1	Friday	Office Supplies	51147.3530
2	Friday	Technology	48756.5820
3	Monday	Furniture	48222.9232
4	Monday	Office Supplies	46030.8700
5	Monday	Technology	40260.3930
6	Saturday	Furniture	31649.8748
7	Saturday	Office Supplies	41986.8600
8	Saturday	Technology	53314.9250
9	Sunday	Furniture	32370.1845
10	Sunday	Office Supplies	38636.0220
11	Sunday	Technology	51708.8700
12	Thursday	Furniture	33842.8408
13	Thursday	Office Supplies	45977.6760
14	Thursday	Technology	53414.0170
15	Tuesday	Furniture	15241.7184
16	Tuesday	Office Supplies	18313.2900
17	Tuesday	Technology	16263.9100
18	Wednesday	Furniture	11119.0680
19	Wednesday	Office Supplies	4005.1040
20	Wednesday	Technology	8012.1140

	Activity
State
California	197
New York	107
Texas	99
Illinois	61
Pennsylvania	53
Ohio	50
Washington	45
Florida	41
Virginia	31
Arizona	25
North Carolina	24
Michigan	22
Georgia	15
Tennessee	13
Colorado	12
Delaware	11
New Jersey	11
Kentucky	11
Oregon	11
Indiana	11
Massachusetts	10
Minnesota	9
Wisconsin	8
Connecticut	8
Utah	8
Alabama	7
Arkansas	7
Missouri	6
Nevada	6
South Carolina	6
Louisiana	5
Mississippi	5
Maryland	5
New Mexico	4
Nebraska	4
Oklahoma	4
Kansas	3
Rhode Island	3
Montana	3
South Dakota	2
Idaho	2
New Hampshire	2
Maine	1
Iowa	1

	Activity
Region
West	608
East	493
Central	443
South	328

	Activity
Region
West	587
East	586
Central	452
South	326

	Activity
Region
West	716
East	701
Central	565
South	393

	Activity
Region
West	952
East	844
Central	711
South	481

	Activity
City
New York City	77
Los Angeles	68
San Francisco	59
Philadelphia	49
Houston	40
...	...
Naperville	1
Nashville	1
Des Plaines	1
Delray Beach	1
Palm Coast	1

	Month_of_order	CategoryName	Sales Revenue
0	April	Furniture	7944.8370
1	April	Office Supplies	11155.0740
2	April	Technology	9195.4340
3	August	Furniture	7320.3465
4	August	Office Supplies	11379.4620
5	August	Technology	9209.6600
6	December	Furniture	30645.9665
7	December	Office Supplies	18006.4620
8	December	Technology	20893.1920
9	February	Furniture	1839.6580
10	February	Office Supplies	1071.7240
11	February	Technology	1608.5100
12	January	Furniture	6242.5250
13	January	Office Supplies	4851.0800
14	January	Technology	3143.2900
15	July	Furniture	10821.0510
16	July	Office Supplies	15121.2080
17	July	Technology	8004.1340
18	June	Furniture	13206.1256
19	June	Office Supplies	12953.0370
20	June	Technology	8435.9650
21	March	Furniture	14573.9560
22	March	Office Supplies	8605.8790
23	March	Technology	32511.1740
24	May	Furniture	6912.7870
25	May	Office Supplies	7135.6240
26	May	Technology	9599.8760
27	November	Furniture	21564.8727
28	November	Office Supplies	26862.4360
29	November	Technology	30201.4080
30	October	Furniture	12304.2470
31	October	Office Supplies	7211.1280
32	October	Technology	11938.0180
33	September	Furniture	23816.4808
34	September	Office Supplies	27423.2980
35	September	Technology	30537.5720

	Month_of_order	CategoryName	Sales Revenue
0	April	Furniture	10475.6985
1	April	Office Supplies	12558.5580
2	April	Technology	11160.9520
3	August	Furniture	9638.5922
4	August	Office Supplies	11735.1080
5	August	Technology	15524.6320
6	December	Furniture	23085.8192
7	December	Office Supplies	16201.6740
8	December	Technology	35632.0280
9	February	Furniture	3134.3740
10	February	Office Supplies	5368.0670
11	February	Technology	3448.9700
12	January	Furniture	11739.9416
13	January	Office Supplies	1808.7800
14	January	Technology	4625.3540
15	July	Furniture	13674.4200
16	July	Office Supplies	4719.9390
17	July	Technology	10370.9660
18	June	Furniture	7714.1790
19	June	Office Supplies	10647.7470
20	June	Technology	6435.3660
21	March	Furniture	12499.7830
22	March	Office Supplies	15882.5470
23	March	Technology	10343.9220
24	May	Furniture	9374.9505
25	May	Office Supplies	9113.7360
26	May	Technology	11643.0000
27	November	Furniture	30880.8325
28	November	Office Supplies	21218.1300
29	November	Technology	23873.6010
30	October	Furniture	12026.6235
31	October	Office Supplies	8673.4100
32	October	Technology	10704.8900
33	September	Furniture	26273.0230
34	September	Office Supplies	19305.7670
35	September	Technology	19017.1280

	Month_of_order	CategoryName	Sales Revenue
0	April	Furniture	13212.0900
1	April	Office Supplies	10647.4470
2	April	Technology	14890.5020
3	August	Furniture	12483.2323
4	August	Office Supplies	8959.7400
5	August	Technology	9672.4020
6	December	Furniture	36678.7150
7	December	Office Supplies	37997.5660
8	December	Technology	22322.7620
9	February	Furniture	3925.5510
10	February	Office Supplies	6794.3500
11	February	Technology	12258.9140
12	January	Furniture	7622.7430
13	January	Office Supplies	5299.6820
14	January	Technology	5620.0660
15	July	Furniture	13068.5190
16	July	Office Supplies	12924.4440
17	July	Technology	13269.0000
18	June	Furniture	13070.5720
19	June	Office Supplies	10901.8100
20	June	Technology	16372.1520
21	March	Furniture	12801.0920
22	March	Office Supplies	17346.9310
23	March	Technology	21567.8520
24	May	Furniture	15119.8350
25	May	Office Supplies	13035.2020
26	May	Technology	28832.6910
27	November	Furniture	31783.6288
28	November	Office Supplies	20487.2780
29	November	Technology	27141.0590
30	October	Furniture	11872.5770
31	October	Office Supplies	16281.7940
32	October	Technology	31533.3740
33	September	Furniture	27262.8809
34	September	Office Supplies	23263.7380
35	September	Technology	22883.4060

	Month_of_order	CategoryName	Sales Revenue
0	April	Furniture	9065.9581
1	April	Office Supplies	15072.1910
2	April	Technology	12383.3870
3	August	Furniture	15441.8740
4	August	Office Supplies	30059.8520
5	August	Technology	17619.1620
6	December	Furniture	31407.4668
7	December	Office Supplies	30436.9420
8	December	Technology	21984.9100
9	February	Furniture	6866.3374
10	February	Office Supplies	7407.7720
11	February	Technology	6027.0240
12	January	Furniture	5964.0320
13	January	Office Supplies	21274.2890
14	January	Technology	16733.0530
15	July	Furniture	11813.0220
16	July	Office Supplies	10241.4680
17	July	Technology	23209.9260
18	June	Furniture	19008.5867
19	June	Office Supplies	16912.0390
20	June	Technology	17061.1000
21	March	Furniture	10893.4448
22	March	Office Supplies	14550.2860
23	March	Technology	33428.6220
24	May	Furniture	16957.5582
25	May	Office Supplies	13736.9640
26	May	Technology	13566.5880
27	November	Furniture	37056.7150
28	November	Office Supplies	31472.3370
29	November	Technology	49918.7730
30	October	Furniture	21884.0682
31	October	Office Supplies	23037.1920
32	October	Technology	32855.6630
33	September	Furniture	29028.2060
34	September	Office Supplies	31895.8430
35	September	Technology	26942.6030

	Month_of_order	CategoryName	Profit
0	April	Furniture	554.3801
1	April	Office Supplies	1000.2086
2	April	Technology	1934.2465
3	August	Furniture	663.4717
4	August	Office Supplies	2767.6007
5	August	Technology	1887.0326
6	December	Furniture	2011.8198
7	December	Office Supplies	2064.3953
8	December	Technology	4907.3548
9	February	Furniture	120.6917
10	February	Office Supplies	176.0910
11	February	Technology	565.5257
12	January	Furniture	805.4665
13	January	Office Supplies	788.9506
14	January	Technology	855.7736
15	July	Furniture	374.2467
16	July	Office Supplies	-2482.0172
17	July	Technology	1266.2879
18	June	Furniture	596.3153
19	June	Office Supplies	3031.4326
20	June	Technology	1348.7765
21	March	Furniture	-1128.6550
22	March	Office Supplies	1270.4133
23	March	Technology	356.9716
24	May	Furniture	-349.8776
25	May	Office Supplies	1747.2044
26	May	Technology	1341.3828
27	November	Furniture	-297.8958
28	November	Office Supplies	4411.9560
29	November	Technology	5178.0667
30	October	Furniture	249.1645
31	October	Office Supplies	1117.6406
32	October	Technology	2081.4522
33	September	Furniture	1858.5976
34	September	Office Supplies	6699.5402
35	September	Technology	-230.0384

	Month_of_order	CategoryName	Profit
0	April	Furniture	338.6502
1	April	Office Supplies	1477.3596
2	April	Technology	2371.4864
3	August	Furniture	-487.4709
4	August	Office Supplies	2208.7531
5	August	Technology	3634.5262
6	December	Furniture	428.8141
7	December	Office Supplies	2631.9409
8	December	Technology	4956.2109
9	February	Furniture	394.8948
10	February	Office Supplies	1814.1018
11	February	Technology	604.8542
12	January	Furniture	-3014.2000
13	January	Office Supplies	589.8969
14	January	Technology	-856.7039
15	July	Furniture	-325.0938
16	July	Office Supplies	1113.7829
17	July	Technology	2499.9592
18	June	Furniture	-412.6597
19	June	Office Supplies	2195.3864
20	June	Technology	1552.8305
21	March	Furniture	1455.0573
22	March	Office Supplies	5407.8482
23	March	Technology	2869.1923
24	May	Furniture	900.5847
25	May	Office Supplies	1731.6139
26	May	Technology	2035.6704
27	November	Furniture	2992.0240
28	November	Office Supplies	2800.0289
29	November	Technology	6682.7355
30	October	Furniture	-605.5163
31	October	Office Supplies	1090.9375
32	October	Technology	2331.9448
33	September	Furniture	1350.1185
34	September	Office Supplies	2037.8837
35	September	Technology	4821.1605

	Month_of_order	CategoryName	Profit
0	April	Furniture	364.8334
1	April	Office Supplies	-1100.4821
2	April	Technology	3713.4636
3	August	Furniture	-494.1457
4	August	Office Supplies	1629.1913
5	August	Technology	927.0237
6	December	Furniture	2828.6715
7	December	Office Supplies	11466.6686
8	December	Technology	3589.9692
9	February	Furniture	377.0352
10	February	Office Supplies	1330.0009
11	February	Technology	3297.5434
12	January	Furniture	303.9607
13	January	Office Supplies	1604.4712
14	January	Technology	916.3914
15	July	Furniture	1038.9073
16	July	Office Supplies	1123.9602
17	July	Technology	2270.0104
18	June	Furniture	-106.2665
19	June	Office Supplies	2467.4894
20	June	Technology	2389.1552
21	March	Furniture	-555.2680
22	March	Office Supplies	1061.4056
23	March	Technology	3105.8304
24	May	Furniture	1824.4739
25	May	Office Supplies	2397.5024
26	May	Technology	4440.1701
27	November	Furniture	819.8088
28	November	Office Supplies	4686.9250
29	November	Technology	-1495.3263
30	October	Furniture	-144.6600
31	October	Office Supplies	4680.0550
32	October	Technology	11707.7475
33	September	Furniture	702.6025
34	September	Office Supplies	3714.0417
35	September	Technology	4912.0134

	Month_of_order	CategoryName	Profit
0	April	Furniture	202.4624
1	April	Office Supplies	3370.6015
2	April	Technology	-2639.7739
3	August	Furniture	322.2390
4	August	Office Supplies	6067.8582
5	August	Technology	2650.8585
6	December	Furniture	1146.7548
7	December	Office Supplies	1774.3132
8	December	Technology	5562.2788
9	February	Furniture	-199.0421
10	February	Office Supplies	953.8253
11	February	Technology	859.0888
12	January	Furniture	-39.4402
13	January	Office Supplies	3260.4467
14	January	Technology	3919.4326
15	July	Furniture	324.6244
16	July	Office Supplies	2563.2845
17	July	Technology	4064.7123
18	June	Furniture	904.9956
19	June	Office Supplies	3378.9821
20	June	Technology	3939.3580
21	March	Furniture	1000.8532
22	March	Office Supplies	2715.9624
23	March	Technology	11035.0759
24	May	Furniture	-72.8829
25	May	Office Supplies	3153.7260
26	May	Technology	3261.7397
27	November	Furniture	406.0637
28	November	Office Supplies	3609.1029
29	November	Technology	5674.9371
30	October	Furniture	-2526.9203
31	October	Office Supplies	4040.7751
32	October	Technology	7761.4207
33	September	Furniture	1548.6837
34	September	Office Supplies	4847.7438
35	September	Technology	4595.1281